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Litho-NeuralODE: Improving Hotspot Detection Accuracy with Advanced Data Augmentation and Neural Ordinary Differential Equations

Published: 07 September 2020 Publication History

Abstract

The use of deep neural networks in pattern matching has tremendously improved the accuracy of the lithographic hotspot detection, preventing any catastrophic chip failure. In this paper, we propose three data augmentation techniques ("Translation", "Gaussian noise", and "Fill shapes") to deal with the imbalance outlier lithographic hotspot problem and adopt the neural ordinary differential equations networks (Litho-NeuralODE) to improve the detection accuracy. Our architecture uses 28x28 pixel clips to perform the hotspot classification. Experimental result on ICCAD 2012 Contest benchmarks shows that our proposed framework achieves the overall highest accuracy of 98.7% and the lowest misses of 10 on average, outperforming the state-of-the-art works.

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References

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Cited By

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  • (2022)Predictable Coupling Effect Model for Global Placement Using Generative Adversarial Networks With an Ordinary Differential Equation SolverIEEE Transactions on Circuits and Systems II: Express Briefs10.1109/TCSII.2021.313608469:4(2261-2265)Online publication date: Apr-2022
  • (2022)WDP-BNNIntegration, the VLSI Journal10.1016/j.vlsi.2022.04.00385:C(76-86)Online publication date: 1-Jul-2022
  • (2022)Litho-NeuralODE 2.0: Improving hotspot detection accuracy with advanced data augmentation, DCT-based features, and neural ordinary differential equationsIntegration10.1016/j.vlsi.2022.02.01085(10-19)Online publication date: Jul-2022
  • Show More Cited By

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  1. Litho-NeuralODE: Improving Hotspot Detection Accuracy with Advanced Data Augmentation and Neural Ordinary Differential Equations

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    cover image ACM Other conferences
    GLSVLSI '20: Proceedings of the 2020 on Great Lakes Symposium on VLSI
    September 2020
    597 pages
    ISBN:9781450379441
    DOI:10.1145/3386263
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    New York, NY, United States

    Publication History

    Published: 07 September 2020

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    Author Tags

    1. deep neural network
    2. design for manufacturability
    3. feature extraction
    4. lithography hotspot detection

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    • Research-article

    Funding Sources

    • National Key Research and Development Program of China
    • Science, Technology and Innovation Action Plan of Shanghai Municipality, China
    • National Science Foundation of China

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    GLSVLSI '20
    GLSVLSI '20: Great Lakes Symposium on VLSI 2020
    September 7 - 9, 2020
    Virtual Event, China

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    Overall Acceptance Rate 312 of 1,156 submissions, 27%

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    Cited By

    View all
    • (2022)Predictable Coupling Effect Model for Global Placement Using Generative Adversarial Networks With an Ordinary Differential Equation SolverIEEE Transactions on Circuits and Systems II: Express Briefs10.1109/TCSII.2021.313608469:4(2261-2265)Online publication date: Apr-2022
    • (2022)WDP-BNNIntegration, the VLSI Journal10.1016/j.vlsi.2022.04.00385:C(76-86)Online publication date: 1-Jul-2022
    • (2022)Litho-NeuralODE 2.0: Improving hotspot detection accuracy with advanced data augmentation, DCT-based features, and neural ordinary differential equationsIntegration10.1016/j.vlsi.2022.02.01085(10-19)Online publication date: Jul-2022
    • (2021)Enhancements of Model and Method in Lithography Hotspot Identification2021 Design, Automation & Test in Europe Conference & Exhibition (DATE)10.23919/DATE51398.2021.9473963(102-107)Online publication date: 1-Feb-2021

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